Title:Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques

Abstract: Diabetic Foot Ulcers (DFU) detection using computerized methods is an
emerging research area with the evolution of machine learning algorithms.
However, existing research focuses on detecting and segmenting the ulcers.
According to DFU medical classification systems, i.e. University of Texas
Classification and SINBAD Classification, the presence of infection (bacteria
in the wound) and ischaemia (inadequate blood supply) has important clinical
implication for DFU assessment, which were used to predict the risk of
amputation. In this work, we propose a new dataset and novel techniques to
identify the presence of infection and ischaemia. We introduce a very
comprehensive DFU dataset with ground truth labels of ischaemia and infection
cases. For hand-crafted machine learning approach, we propose new feature
descriptor, namely Superpixel Color Descriptor. Then, we propose a technique
using Ensemble Convolutional Neural Network (CNN) model for ischaemia and
infection recognition. The novelty lies in our proposed natural
data-augmentation method, which clearly identifies the region of interest on
foot images and focuses on finding the salient features existing in this area.
Finally, we evaluate the performance of our proposed techniques on binary
classification, i.e. ischaemia versus non-ischaemia and infection versus
non-infection. Overall, our proposed method performs better in the
classification of ischaemia than infection. We found that our proposed Ensemble
CNN deep learning algorithms performed better for both classification tasks
than hand-crafted machine learning algorithms, with 90% accuracy in ischaemia
classification and 73% in infection classification.